stata代写-ECN 140
时间:2021-03-04
ECN 140 Professor Shu Shen

Winter 2021, Empirical Project


Requirements:

1. Due on March 8th at the end of the day on Canvas. Both your TA and I
will grade your report. The average will be the score for your
empirical project. There is no regrading of the empirical project.
2. Use Stata to analyze the provided dataset.
3. Summarize your results in a report. Describe: your question(s), your
data (e.g., show features of the variables of the full sample or across
different subsamples using summary statistics and figures), your
regression models, your estimates with standard errors (in tables),
and the answers to your questions. Remember to interpret your
regression results carefully. Pay attention to both statistical
significance and economic significance.
4. The report shall be no more than 10 pages long (double spaced time
new roman font size 12). Be sure not to include unnecessary Stata
outputs in your report. Use equations and make your own tables (not
Stata output tables) to summarize your regression results.
5. Submit your Stata log file together with the report. Make sure that
your results could be replicated by the commands in you log file.
6. Keep in mind that this is a report for an econometrics class. Asking
the right question is important. But to demonstrate that you know
what econometrics models to use and how to properly interpret the
regression results is important as well.
7. Run your report through some online grammar editing website before
submission. Although we won’t be picky in language and grammar,
well-written papers generally make good impressions.



Background of the Stata data file

In November 1984, the Kentucky Public Service Commission (KPSC)
established Administrative Case No. 285 to study the economic feasibility of
providing local–measured–service telephone rates in Bowling Green and
Louisville, Kentucky. We are only looking at the data in Louisville in this
project.

The experiment took place in the second half of 1986. Before the start date,
Louisville consumers were instructed to choose between unlimited calls at a
cost of $18.70 per month, or a measured service option with a monthly fee
of $14.02. The measured service tariff included a $5 allowance and
distinguished setup, duration, peak periods, and distance. The tariff
differentiated among three periods: peak was from 8 a.m. to 5 p.m. on
weekdays; shoulder was between 5 p.m. to 11 p.m. on weekdays and
Sundays; and off–peak was any other time. For distance band A, measured
charges in peak periods were 2 cents for setup and 2 cents per minute. These
charges had a 35% discount in shoulder time and 60% discount in off–peak
time. For distance band B, setup charges remained equal but duration
always had a 100% surcharge relative to the corresponding tariff for band A.
The following table summarizes the setup and duration charges for those
who choose the measured service option.

Setup Charge per call Additional charge Per Minute
Band A, Peak 2 cents 2 cents
Band A, Shoulder 2 cents * (1-35%) 2 cents * (1-35%)
Band A, Off-peak 2 cents * (1-60%) 2 cents * (1-60%)
Band B, Peak 2 cents 2 cents * 2
Band B, Shoulder 2 cents * (1-35%) 2 cents * (1-35%)* 2
Band B, Off-peak 2 cents * (1-60%) 2 cents * (1-60%)* 2

If set-up and minute charges do not add up to $5 in a given month, the
household pays $14.02 for that month. If set-up and minute charges exceed
$5 in a given month, the household pays $14.02+(the total of set-up and
minute charges-$5).

The dataset contains randomly sampled households in Louisville. Household
demographic information is surveyed before the experiment took place in
July 1986. Household detailed telephone usage is based on administrative
files recorded by KPSC. In this dataset, we only use household telephone
usage information from Oct. to Dec. i.e., 3 months after the beginning of the
experiment to make sure that households had all been aware of their new
tariff schedules.
Demographic variables include:
ID Household ID

MONTH Month of telephone usage in document

AGE =1 if 15-14
=2 if 25-34
=3 if 35-44
=4 if 45-54
=5 if 55-64
=6 if >65

HHSIZE # in household

ADULTS # of adults in household

TEENS # of kids in household

SENIORS # of seniors in household

EDUC education of household head
=1 if high school drop out
=2 if high school graduate
=3 if some college
=4 if college graduate
=5 if some graduate school
=6 if graduate school

MARRIED marital status of household head
=1 if married
=2 if widowed, divorced, separated,
=3 if never married

RACE race of household head
=1 if white
=2 if black
=3 if spanish
=4 if asian
=5 if other

INCOME annual household income
=1 if < $5000
=2 if $5000-7499
=3 if $7500-9999
=4 if $10000-14999
=5 if $15000-19999
=6 if $20000-24999
=7 if $25000-34999
=8 if $35000-49999
=9 if > $50000

BENEFITS =1 if receiving benefits such as social security,
food stamps or aid to dependent children, =0 if otherwise.

LV =1 if the household resides in Louisville. In this
dataset, it’s always 1.

Telephone usage variables include the tariff variable and a bunch of usage
pattern variables.
Tariff =1 if measured
=0 if flat
Usage pattern variables are named according to the following system of
rules:

First character: M=minutes C=number of calls
Second character: A=band A (close) B=band B (far)
Third character: P= (peak) Weekdays, from 8am to 5pm
S= (shoulder) Weekdays and Sundays, from 5pm to 11pm
O= (off-peak) Weekdays from 11pm to 8am and from
Fridays 11pm to Sundays 5pm

Some Notes (with couple questions for you to start with):

This data file includes a lot of information. You don’t need to use all of it.
You could start from pooling all peak calls and minutes, all shoulder calls
and minutes, as well as all off-peaks calls and minutes together and ask
questions like “Did low income people make more of their calls during off-
peak hours?”, “Did younger households call more or do elder households
call more?”, “Did household call more during the holiday season?”, “Did
households who had chosen the flat rate schedule and those who had chosen
the tariff schedule have similar demographic characteristics?”, “Did
households under measured service rate make less peak hour calls than those
under flat rate? Did they make more or less distance band B calls during
peak hours? Did they make shorter distance band B calls because of the
duration surcharge? Did these households utilize almost all of the $5
allowance?”
If you are willing to program a bit in Stata, you could also recover
household's monthly bill from the information in the dataset. Then you ask
even more interesting questions related to households’ choice between the
two telephone plans.


Final Notes:

Most real world survey datasets are kind of messy. I have cleaned the dataset
before I gave it to you. But if you spot anything that is suspicious, just
discuss it in a footnote of your report.




























































































































































































































































































































































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